A DNA-based optical force sensor for live-cell applications
Jayachandran, C.; Ghosh, A.; Prabhune, M.; Bath, J.; Turberfield, A. J.; Hauke, L.; Enderlein, J.; Rehfeldt, F.; Schmidt, C. F.
Show abstract
Mechanical forces are relevant for many biological processes, from wound healing or tumour formation to cell migration and differentiation. Cytoskeletal actin is largely responsible for responding to forces and transmitting them in cells, while also maintaining cell shape and integrity. Here, we describe a novel approach to employ a FRET-based DNA force sensor in vitro and in cellulo for non-invasive optical monitoring of intracellular mechanical forces. We use fluorescence lifetime imaging to determine the FRET efficiency of the sensor, which makes the measurement robust against intensity variations. We demonstrate the applicability of the sensor by monitoring cross-linking activity in in vitro actin networks by bulk rheology and confocal microscopy. We further demonstrate that the sensor readily attaches to stress fibers in living cells which opens up the possibility of live-cell force measurements.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Designed Anchoring Geometries Determine Lifetimes of Biotin-Streptavidin Bonds under Constant Load and Enable Ultra-Stable Coupling 95%
- Transport among protocells via tunneling nanotubes 95%
- Cooperative dynamics of DNA grafted magnetic nanoparticles optimize magnetic biosensing and coupling to DNA origami 95%
Similar papers in this journal
Similar papers in this journal
- Genetically Encoded RNA-based Bioluminescence Resonance Energy Transfer (BRET) Sensors 94%
- Three-dimensional tracking of tethered particles for probing nanometer-scale single-molecule dynamics using plasmonic microscope 94%
- Aptamer-antibody chimera sensors for sensitive, rapid and reversible molecular detection in complex samples 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.